Application of remote sensing, GIS and MCA techniques for delineating groundwater prospect zones in Kashipur block, Purulia district, West Bengal
- 777 Downloads
Demand of groundwater resources has increased manifold with population expansion as well as with the advent of modern civilization. Assessment, planning and management of groundwater resource are becoming crucial and extremely urgent in recent time. The study area belongs to Kashipur block, Purulia district, West Bengal. The area is characterized with dry climate and hard rock terrain. The objective of this study is to delineate groundwater potential zone for the assessment of groundwater availability using remote sensing, GIS and MCA techniques. Different thematic layers such as hydrogeomorphology, slope and lineament density maps have been transformed to raster data in TNT mips pro2012. To assign weights and ranks to different input factor maps, multi-influencing factor (MIF) technique has been used. The weights assigned to each factor have been computed statistically. Weighted index overlay modeling technique was used to develop a groundwater potential zone map with three weighted and scored parameters. Finally, the study area has been categorized into four distinct groundwater potential zones—excellent 1.5% (6.45 sq. km), good 53% (227.9 sq. km), moderate 45% (193.5 sq. km.) and poor ~ 0.5% (2.15 sq. km). The outcome of the present study will help local authorities, researchers, decision makers and planners in formulating proper planning and management of groundwater resources in different hydrogeological situations.
KeywordsRemote sensing and GIS analysis Multi-criteria analysis (MCA) Weighted index overlay (WIOA) Groundwater potential zone Kashipur block Purulia district
The rapid development of this groundwater resource for many purposes has resulted in expansion of irrigation and overall economic development, and thereby improving the lifestyle in India. Groundwater is the main source for more than 85% of rural domestic supply and it is rapidly depleting in many areas owing to large-scale withdrawal. The significant contribution made for the Green Revolution has further accelerated the rapid decline in groundwater levels. This alarming situation calls for a cost- and time-effective technique for proper evaluation of groundwater resources and management planning. A groundwater development program requires a large volume of data from various sources. Remote sensing and GIS study with its advantages of spatial, spectral and temporal availability of data covering large and inaccessible areas within a short time has come out as an efficient tool to provide the appropriate platform for convergent analysis of large volumes of multi-disciplinary data and decision making for groundwater studies. Geospatial technology is a rapid and cost-effective tool in producing valuable data on geology, geomorphology, lineaments and slope, etc., that plays a significant role in deciphering groundwater potential zone. The spatially complete and temporal nature of the remote sensing data provides excellent opportunities to hydrogeologists for improving the understanding of the hydrogeological system (Hoffmann and Sander 2007) in any area. Several factors such as geomorphology, land use/land cover, geological structures, fractures, groundwater table distribution, slope and drainage—all contribute in the movement and occurrence of groundwater in an area (Arkoprova et al. 2012).
Several conventional methods such as geological, hydrogeological, geophysical and photogeological techniques are being used to delineate groundwater potential zones (McNeill 1991; Lillesand and Kiefer 1994; Teeuw 1995; Meijerink 1996; Sander et al. 1996; Edet et al. 1997; Taylor and Howard 2000; Shahid et al. 2000; Srivastava and Bhattacharya 2006; Sharma and Kaur 2012; Fashae et al. 2013; Selvam et al. 2014; Jhariya et al. 2016). In recent times, digital techniques come out as a rapid and cost-effective tool, and are being used to integrate different data to decipher groundwater potential zones. In recent times, many workers have used the approach of RS and GIS for delineation of groundwater potential zones and artificial recharge sites (Chatterjee and Bhattacharya 1995; Teeuw 1995; Krishnamurthy et al. 1996; Saraf and Choudhary 1998; Shahid and Nath 1999; Goyal et al. 1999; Murthy 2000; Obi et al. 2000; Pratap et al. 2000; Shahid et al. 2000; Jaiswal et al. 2003; Srinivasa and Jugran 2003; Kamal and Midorikawa 2004; Nag 2005; Sener et al. 2005; Sreedevi et al. 2005; Gustavsson et al. 2006; Srivastava and Bhattacharya 2006; Singh et al. 2007 Jasrotia et al.2007; Kumar et al. 2007; Chenini et al. 2010; Machiwal et al. 2011; Talabi and Tijani 2011; Kuria et al. 2012; Gumma and Pavelic 2013; Nag and Ghosh 2013; Nag and Saha 2014; Olutoyin 2014; Nag and Ray 2015). Furthermore, GIS is also being used for multi-criteria analysis for groundwater resource evaluation (Saraf et al. 2004; Rao and Jugran 2003). Multi-criteria analysis (MCA) process provides an optimum solution in which the uncertainties associated with evaluating criteria are ranked on the basis of overall performance of various input with respect to the multiple objectives (Aher et al. 2013). In MCA, each and every criterion is given a weightage to represent its genuine importance (Chow and Sadler 2010). The integration of GIS and MCA techniques provides efficient spatial analysis functions (Yu et al. 2009). In recent times, GIS-based MCA technique has gained importance because of its capacity to integrate a large quantity of data (Chen et al. 2010; Feizizadeh et al. 2012; Kumar and Jhariya 2015).
The present study aims to contribute towards systematic groundwater studies utilizing Remote Sensing, Field Studies, Digital Elevation Modeling (DEM) and GIS in developing effective guidelines for groundwater resource management in similar hydrological terrains. The specific objective of the study is to delineate groundwater potential zones. Since the area comprised crystalline basement, groundwater occurs mostly in shallow weathered residuum and in fractured bedrock. Owing to the failure of public/municipal water supply scheme, the people had to depend on the groundwater resources and consequently the water level is gradually declining. To arrest this gradual and continued declining of water level, indiscriminate use of groundwater by putting dug cum bore wells, it is of utmost importance to have a proper understanding of the hydrogeological situation as well as groundwater potentiality of the area. This methodology can also be applied effectively in the areas with similar climate and geology such as southern India, which suffers from acute shortage of water leading to severe suffering of farmers.
Data used and methodology
Preparation of district and village boundary maps.
Preparation of hydrogeomorphological map.
Preparation of Slope map.
Preparation of Lineament map.
Preparation of Lineament density map.
Assignment of weights according to their respective importance in groundwater occurrence and,
Finally, the thematic layers are integrated using TNT mips pro2012 software to generate groundwater potential zone map of the study area. Accordingly, four different zones, namely ‘very good’, ‘good’, ‘poor’, and ‘very poor’ have been identified.
Initially from satellite images and toposheets, lineament map, hydrogeomorphological map, contour map and slope map were prepared. By visually interpreting the satellite imagery, the lineaments of the study area are picked up and traced on the basis of tonal, textural, soil, vegetation, topographic and drainage linearity, curvilinear ties and rectilinear ties (Lilesand 1989; Drury 1990; Gupta 1991). In the study area, major lineaments are identified from the satellite data interpretation, which are surface manifestation of some structural features in the bedrock as fracture and joints developed due to tectonic stress and strain. Fracture and joints are usually visible in rock exposures though not all are always identified at every outcrop. Joints may develop in more than one set and with varying frequency in exposures.
The slope or gradient of a line describes its steepness, incline, or grade. A higher slope value indicates a steeper incline. The slope is defined as the ratio of the “rise” divided by the “run” between two points on a line, or in other words, the ratio of the altitude change to the horizontal distance between any two points on the line. The angle θ of a line makes with the positive x axis is closely related to the slope m via the tangent function: m = tan θ.
For preparing slope map of the area digital elevation model (DEM) Cartosat − 1 (30 m) resolution is used and developed by TNT mips spatial analysis tools.
- xi.All the maps were converted into raster format and georeferenced to common reference point in the Universal Transverse Mercator plane coordinate system. All the themes were integrated using “Spatial Analyst Module” of TNT mips pro 2012. Each theme and its individual class were assigned weight and rank (Table 1) based on the existing literature (Saraf and Choudhary 1998; Rao and Jugran 2003; Prasad et al. 2008; Jha et al. 2010; Machiwal et al. 2011; Mukherjee et al. 2012; Singh et al. 2013). Individual themes have been assigned weights (Wt) and based on the knowledge upon their significance to groundwater, different ranks were given (Wi) to each features within the theme. Then factor scores were derived for each feature by multiplying theme weight (Wt) with feature rank (Wi). In the same way, scores have been derived for all the themes. Subsequently, themes have been converted into raster format so that each pixel contains factor scores with respect to their potentiality to groundwater. Finally, all these thematic layers have been integrated and the total factor scores for individual pixel have been calculated through raster addition process in Spatial Analyst Extension of TNT Mips. Based upon derived scores, the final integrated map was prepared.Table 1
Weightage of different parameters for groundwater prospect zonation
Weight (W i )
Ground water prospects
Rank (R i )
Buried pediment moderate
Buried pediment shallow
Moderate to good
Moderate to poor
Good to moderate
The resultant composite coverage was classified into four groundwater potential zones: (1) excellent, (2) good, (3) moderate and (4) poor. The output map was correlated and validated with the field groundwater data.
Results and discussion
Different thematic maps describing favorable sites for groundwater potentiality have been prepared. These maps were then integrated with the help of GIS software TNTmips pro 2012, into a single map showing groundwater potential zones in the study area.
Different hydrogeomorphic units, their characteristic features along with groundwater potentiality
Valley fills (VF)
Accumulation zone of colluvial materials derived from the surrounding uplands; shallow to deep; fine loamy to clayey soils
Buried pediment moderate (BPM)
Gently sloping topography; moderately deep, clayey to fine loamy soils. Because of low relief shallow depressed area, infiltration is moderate to good
Buried pediment shallow (BPS)
Nearly flat to gently sloping topography, shallow to moderately deep, loamy soils followed by regolith zone. Intermediate zone between pediments and deep pediments, infiltration is moderate
Residual hills (RH)
Elevated portions of the hill but steeply sloping on all directions below plateau level. Mostly run-off, but along fractures/joints moderate to poor; dug wells along fractures/joints may be made
Drainage network analysis is important for geo-hydrological studies. Drainage pattern reflects the characteristic of surface as well as subsurface formation. The study area is traversed by a number of rivers. The Dwarakeswar river is one of the largest river in the study area and flows from northwest to south east. Drainage density is defined as the closeness of spacing of stream channels. It is a measure of the total length of the stream segment of all orders per unit area. The drainage density is an inverse function of permeability. If a rock is less permeable, the lesser is the infiltration of rainfall, which conversely means high surface run-off. In the present study, since the drainage density can indirectly indicate the groundwater potential of an area due to its relation to surface run-off and permeability, it was considered as one of the indicators of groundwater occurrence. The drainage density of the study area has been calculated as 0.20 km/km2.
Lineament and lineament density
Lineaments are topography of the underlying linear structural features. Lineaments play an important role in groundwater recharge in hard rock terrains; groundwater potential is high near lineament zones Srivastava and Bhattacharya (2006). In hard rock terrains, lineaments represent areas and zones of faulting and fracturing, resulting in increased secondary porosity and permeability and are good indicators of groundwater (Kumar et al. 2007). Well-developed fractures/lineaments intersecting with each other can hold appreciable quantity of groundwater. Lineaments provide pathways for groundwater movements and are hydrogeologically very important. The lineaments intersections are considered as good groundwater potential zones.
Lineament delineated using satellite imageries, with azimuth sector size is 5° were converted into zones of different lineament densities, viz. very high to high (1) weightage factor, high to moderate (2), moderate to low (3), and low to very low (4) using spatial density analysis in GIS domain.
Groundwater potential zone delineation
A multiparametric approach (Skankar and Mohan 2006) has been considered for delineating groundwater potential zone (GWPZ). The parameters or factors used for GWPZ consist of three main influencing factors, such as hydrogeomorphology, slope and lineament. Each factor has been assigned a weighted according to its strength.
Groundwater table distribution
Groundwater potential zones have been delineated in Kashipur block of Purulia district, West Bengal, using Remote Sensing and GIS approach which seems to be very constructive. All the thematic maps were converted into grid (raster format) and analyzed by weighted overlay method (rank and weightage wise thematic maps). From the results of the groundwater potential zone map, Kashipur block of Purulia district has been categorized into four different zones, namely very good (6.45 sq. km that covers only 1.5% of the total block area), good (227.9 sq. km that occupy majority of the block about 53%), poor (193.5 sq. km of the block that occupy about 45% of the total block), and very poor (2.15 sq. km area that is only 0.5%).
To validate the groundwater potential zones delineated through Remote Sensing, GIS and MCA Techniques, data on existing borewells collected from State Water Investigation Directorate (SWID) and Central Ground Water Board (CGWB) of the study area. The results revealed a good correlation with respect to derived groundwater potential zones. High yielding (> 130 m3/day) borewells are located in ‘Excellent’ and ‘good’ groundwater potential zones. Low yielding (< 60 m3/day) borewells are typically found in low groundwater potential zones.
The map thus prepared can serve as guidelines for planning future artificial recharge projects in the study area to ensure sustainable groundwater utilization. Concerned decision makers can formulate an efficient groundwater utilization plan for the study area so as to ensure long-term sustainability.
The author (SKN) gratefully acknowledges the financial support from Centre of Advanced Study (CAS Phase V), Department of Geological Sciences, Jadavpur University in conducting the field work related to this work. The other author (A. Kundu) is thankful to Department of Science and Technology, Govt. of India, New Delhi and Jadavpur University also for providing the DST Inspire Fellowship to her. The authors also acknowledge the reviewers for their painstaking, critical and constructive review of the manuscript which helped us to improve its quality.
- Aher PD, Adinarayana J, Gorantiwar SD (2013) Prioritization of watersheds using multi-criteria evaluation through fuzzy analytical hierarchy process. Agric Eng Int J 15(1):11–18Google Scholar
- Arkoprovo B, Adarsa J, Prakash SS (2012) Delineation of groundwater potential zones using satellite remote sensing and geographic information system techniques: a case study from Ganjam district, Orissa, India. Res J Recent Sci 1(9):59–66Google Scholar
- Bonham-Carter GF (1994) Geographic information systems for geoscientists: modelling with GIS. Elseviers, USAGoogle Scholar
- C. G. W. B. 2007 District wise Report of Central Ground Water Board, Ministry of Water Resources, government of IndiaGoogle Scholar
- Chatterjee RS, Bhattachaya AK (1995) Delineation of drainage pattern of a coal basin related inference using satellite remote sensing techniques. Asia Pac Remote Sens J 1:107–114Google Scholar
- Chen Y, Khan S, Paydar Z (2010) To retire or expand. A fuzzy GIS-based spatial multi-criteria evaluation framework for irrigated agriculture. Irrig Drain 59(2):17–188Google Scholar
- Choudhury PR (1999) Integrated remote sensing and GIS techniques for groundwater studies in part of Betwa Basin. Ph.D. Thesis (unpublished), Department of Earth Sciences, University of Roorkee, IndiaGoogle Scholar
- Drury SA (1990) Image Interpretation in Geology, 2nd edn. Chapman & Hall, LondonGoogle Scholar
- Goyal S, Bharawadaj RS, Jugran DK (1999) Multicriteria analysis using GIS for groundwater resource evaluation in Rawasen and Pilli watershed, U. P. http://wwwGISdevelopment.net. Accessed on 17 Dec 2003
- Kuria DN, Gachari MK, Macharia MW, Mungai E (2012) Mapping groundwater potential in Kitui district, Kenya using geospatial technologies. Int J Water Resour Environ Eng 4(1):15–22Google Scholar
- Lilesand TM (1989) Remote sensing and image interpretation. Wiley, USAGoogle Scholar
- Lillesand TM, Kiefer RW (1994) Remote sensing and image interpretation, 3rd edn. Wiley, New York, p 750Google Scholar
- Muthukrishnan A, Manjunatha V (2008) Role of remote sensing and GIS in artificial recharge of the ground water aquifer in the shanmuganadi sub watershed in the Cauvery River basin, Tiruchirappalli District, Tamil Nadu. International symposium on geoinformatics for spatial infrastructure development in Earth and Allied Sciences, pp 1–6Google Scholar
- Pandian M, Kumanan CJ (2013) Geomatics approach to demarcate groundwater potential zones using remote sensing and GIS techniques in part of Trichy and Karur district, Tamilnadu, India. Appl Water Sci 5(2):234–240Google Scholar
- Rokade VM, Kundal P, Joshi AK (2007) Groundwater potential modeling through remote sensing and GIS: a case study from Rajura Taluka, Chandrapur district, Maharashtra. J Geol Soc 69:943–948Google Scholar
- Saraf AK, Choudhury PR (1997) Integrated application of remote sensing and GIS groundwater exploration in hard rock terrain. In: Proceedings of International Symposium on Emerging trends in Hydrology, Department of Hydrology, Roorkee, September 25–27, 1:435–442Google Scholar
- Sarma B, Saraf AK (2002) Study of landuse—groundwater relationship using an integrated remote sensing and GIS approach. Proceedings of MapAsia 2002, Asian Conference on GIS, GPS, Aerial Photography and Remote Sensing, organised by Asian Institute of Technology, Bangkok and CSDMS, New Delhi, held in Bangkok between 7 and 9 August, 2002Google Scholar
- Shahid S, Nath SK (1999) GIS integration of remote sensing and electrical sounding data for hydrogeological exploration. J Spat Hydrol 2(1):1–12Google Scholar
- Sharma D, Jugran DK (1992) Hydromorphological studies around Pinjaur-Kala Amb area, Ambala district (Hariana), and Sirmour district (Himachal Pradesh). J Indian Soc Remote Sens 29(4):281–286Google Scholar
- Sharma MP, Kaur A (2012) Application of Remote Sensing and GIS for groundwater recharge zone in and around Goal block, Ramgarh district, Jharkhand, India. Int J Sci Res Publ 2(2):1–6Google Scholar
- Singh AK, Parkash B, Choudhury PR (2007) Integrated use of SRM, Landsat ETM+ data and 3D perspective views to identify the tectonic geomorphology of Dehradun valley, India. Int J Remote Sens 28(11):2430–2444Google Scholar
- Subba Rao N (1992) Factors affecting optimum development of groundwaters in crystalline terrain of the eastern Ghats, Visakhapatnam area, Andhra Pradesh, India. J Geol Soc India 40(5):462–467Google Scholar
- Talabi AO, Tijani MN (2011) Integrated remote sensing and GIS approach to Ground-water potential assessment in the basement terrain of Ekiti area south-western Nigeria. RMZ Mater Geoenviron 58(3):303–328Google Scholar
- Yu J, Chean Y, Wu JP (2009) Cellular automata and GIS based land use suitability simulation for irrigated agriculture. 18th world IMACS/MODSIM Congress July, Cairns, Australia, 13–17Google Scholar
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.